Abstract
Most thermal/chemical industries are equipped with heat exchangers to enhance thermal efficiency. The performance of heat exchangers highly depends on design modifications in the tube side, such as the cross-sectional area, orientation, and baffle cut of the tube. However, these parameters do not exhibit a specific relation to determining the optimum design condition for shell and tube heat exchangers with a maximum heat transfer rate and reduced pressure drops. Accordingly, experimental and numerical simulations are performed for a heat exchanger with varying tube geometries. The heat exchanger considered in this investigation is a single-shell, multiple-pass device. A Generalized regression neural network (GRNN) is applied to generate a relation among the input and output process parameters for the experimental data sets. Then, an Artificial immune system (AIS) is used with GRNN to obtain optimized input parameters. Lastly, results are presented for the developed hybrid GRNN-AIS approach.
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Recommended by Associate Editor Ji Hwan Jeong
J. Bala Bhaskara Rao is currently working as an Associate Professor and Head of the Department of Mechanical Engineering at SISTAM College of Engineering, India. Simultaneously, he is pursuing a doctoral degree in JNT Unversity, Kakinda.
B. B. V. L. Deepak is currently working as an Assistant Professor in the Department of Industrial Design at the National Institute of Technology, Rourkela, India. He obtained his M. Tech. and Ph.D. in mechanical engineering from NIT, Rourkela. He is an Editorial Board Member and Reviewer for several reputed indexed journals. Dr. Deepak has published over 40 articles in several peer-reviewed journals, conferences, and book chapters.
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Rao, B.B., Raju, V.R. & Deepak, B.B.V.L. Estimation and optimization of heat transfer and overall pressure drop for a shell and tube heat exchanger. J Mech Sci Technol 31, 375–383 (2017). https://doi.org/10.1007/s12206-016-1239-6
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DOI: https://doi.org/10.1007/s12206-016-1239-6